MSc Artificial Intelligence for Geospatial Data Analysis

1 Year On Campus Masters Program

University of Hull

Program Overview

The MSc Artificial Intelligence for Geospatial Data Analysis at the University of Hull trains students to apply AI and machine learning to spatial, remote-sensing and Earth-observation data. It suits those with a numerate background who want careers in geospatial analytics, environmental data science, or GIS-based AI roles.

Curriculum structure

Students begin with Programming for AI and Data Science, Understanding Artificial Intelligence, and Applied Artificial Intelligence, gaining coding and machine-learning foundations. They then move into geospatial modules such as Fundamentals of Geospatial Data and Earth Observation and Spatio-temporal Big Data Analytics, learning to process and model spatial and satellite-derived data. The course ends with an AI and Data Science Research Project, applying AI to a real geospatial challenge.

Focus areas: “geospatial analytics, machine learning, remote sensing, Earth observation, big spatial data.”
Learning outcomes: “build ML models, analyse spatial/remote-sensing data, code AI solutions, complete an applied geospatial-AI project.”
Professional alignment: Prepares for roles in GIS, environmental analytics, urban planning, and remote-sensing AI.
Reputation: Strong applied focus supporting employability in rapidly growing geospatial-AI sectors.

Experiential Learning (Research, Projects, Internships etc.)

Students gain practical skills by applying AI and machine learning techniques to real-world geospatial datasets, using the University's high-performance computing infrastructure and specialist geospatial software. This applied learning is central to the curriculum:

- Software: Training in Python, R, and GIS software (like ArcGIS Pro and QGIS) with key AI libraries (TensorFlow, PyTorch) for spatial analysis.
- Geospatial Data: Hands-on work with satellite imagery, LiDAR, and GPS data to solve environmental and urban planning challenges.
- Research Project: An individual dissertation focusing on AI applications in geospatial analysis.
- Computing Facilities: Access to the University's High-Performance Computing (HPC) resources for processing large geospatial datasets.

Progression & Future Opportunities

Graduates of the University of Hull's MSc Artificial Intelligence for Geospatial Data Analysis master AI, machine learning, data visualization, and geo-information science, enabling automated analysis of Earth observation data for environmental monitoring, urban planning, and resource management. The program addresses the national shortage of AI data scientists, equipping alumni with practical skills via real-world projects and ethical data handling for high-demand geospatial roles. Typical job roles: Remote Sensing Scientist, Earth Observation Scientist, Geospatial Analyst, Data Engineer.

University services: Careers service provides CV support, interview practice, job fairs, Handshake platform, and industry placements.
Employment stats/salary: High demand in geospatial sector; data scientists start at £35,000–£50,000.
Partnerships: Aura Innovation Centre, Humber Offshore Wind Cluster, flood resilience SMEs.
Accreditation value: Hull's expertise in energy, environment, and AI ensures enduring industry recognition.
Outcomes: Careers in geospatial industries, research, or consulting; strong further study routes.

Further Academic Progression: Pursue PhD in geospatial AI, data science, or environmental analytics at Hull, building on dissertation research.

Additional Information & Requirements

Applicants will require a 2:2 undergraduate honours degree in any subject that includes a numerical component (i.e. statistical analysis, or formal mathematics, or programming in any given language),

India- Lower second-class (2.2) equivalent Overall 50%

USA- Bachelors degree with GPA 2.8 out of 4

IELTS 6.5 overall, with no less than 5.5 in each skill.

TOEFL- Overall 79-93 – At least - Reading 10, Listening 9, Speaking 17, Writing 19

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